Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations3000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1014.7 KiB
Average record size in memory346.3 B

Variable types

Categorical3
Numeric19

Alerts

Population has unique values Unique

Reproduction

Analysis started2025-06-12 16:38:59.938304
Analysis finished2025-06-12 16:39:49.874856
Duration49.94 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Country
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size187.8 KiB
India
319 
Russia
315 
USA
311 
Nigeria
304 
Pakistan
298 
Other values (5)
1453 

Length

Max length12
Median length9
Mean length7.0466667
Min length3

Characters and Unicode

Total characters21140
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndonesia
2nd rowBrazil
3rd rowNigeria
4th rowRussia
5th rowIndonesia

Common Values

ValueCountFrequency (%)
India 319
10.6%
Russia 315
10.5%
USA 311
10.4%
Nigeria 304
10.1%
Pakistan 298
9.9%
Bangladesh 293
9.8%
South Africa 291
9.7%
Brazil 291
9.7%
China 290
9.7%
Indonesia 288
9.6%

Length

2025-06-12T16:39:49.982344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-12T16:39:50.131872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
india 319
9.7%
russia 315
9.6%
usa 311
9.5%
nigeria 304
9.2%
pakistan 298
9.1%
bangladesh 293
8.9%
south 291
8.8%
africa 291
8.8%
brazil 291
8.8%
china 290
8.8%

Most occurring characters

ValueCountFrequency (%)
a 3280
15.5%
i 2700
 
12.8%
n 1776
 
8.4%
s 1509
 
7.1%
d 900
 
4.3%
r 886
 
4.2%
e 885
 
4.2%
h 874
 
4.1%
I 607
 
2.9%
u 606
 
2.9%
Other values (17) 7117
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3280
15.5%
i 2700
 
12.8%
n 1776
 
8.4%
s 1509
 
7.1%
d 900
 
4.3%
r 886
 
4.2%
e 885
 
4.2%
h 874
 
4.1%
I 607
 
2.9%
u 606
 
2.9%
Other values (17) 7117
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3280
15.5%
i 2700
 
12.8%
n 1776
 
8.4%
s 1509
 
7.1%
d 900
 
4.3%
r 886
 
4.2%
e 885
 
4.2%
h 874
 
4.1%
I 607
 
2.9%
u 606
 
2.9%
Other values (17) 7117
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3280
15.5%
i 2700
 
12.8%
n 1776
 
8.4%
s 1509
 
7.1%
d 900
 
4.3%
r 886
 
4.2%
e 885
 
4.2%
h 874
 
4.1%
I 607
 
2.9%
u 606
 
2.9%
Other values (17) 7117
33.7%

Region
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size191.8 KiB
Africa
625 
South America
611 
North America
594 
Asia
590 
Europe
580 

Length

Max length13
Median length6
Mean length8.4183333
Min length4

Characters and Unicode

Total characters25255
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowNorth America
3rd rowSouth America
4th rowAfrica
5th rowEurope

Common Values

ValueCountFrequency (%)
Africa 625
20.8%
South America 611
20.4%
North America 594
19.8%
Asia 590
19.7%
Europe 580
19.3%

Length

2025-06-12T16:39:50.328957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-12T16:39:50.426717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
america 1205
28.7%
africa 625
14.9%
south 611
14.5%
north 594
14.1%
asia 590
14.0%
europe 580
13.8%

Most occurring characters

ValueCountFrequency (%)
r 3004
11.9%
A 2420
9.6%
a 2420
9.6%
i 2420
9.6%
c 1830
 
7.2%
o 1785
 
7.1%
e 1785
 
7.1%
1205
 
4.8%
h 1205
 
4.8%
t 1205
 
4.8%
Other values (8) 5976
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 3004
11.9%
A 2420
9.6%
a 2420
9.6%
i 2420
9.6%
c 1830
 
7.2%
o 1785
 
7.1%
e 1785
 
7.1%
1205
 
4.8%
h 1205
 
4.8%
t 1205
 
4.8%
Other values (8) 5976
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 3004
11.9%
A 2420
9.6%
a 2420
9.6%
i 2420
9.6%
c 1830
 
7.2%
o 1785
 
7.1%
e 1785
 
7.1%
1205
 
4.8%
h 1205
 
4.8%
t 1205
 
4.8%
Other values (8) 5976
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 3004
11.9%
A 2420
9.6%
a 2420
9.6%
i 2420
9.6%
c 1830
 
7.2%
o 1785
 
7.1%
e 1785
 
7.1%
1205
 
4.8%
h 1205
 
4.8%
t 1205
 
4.8%
Other values (8) 5976
23.7%

Income_Level
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size190.1 KiB
Lower-Middle
801 
Low
741 
Upper-Middle
731 
High
727 

Length

Max length12
Median length12
Mean length7.8383333
Min length3

Characters and Unicode

Total characters23515
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUpper-Middle
2nd rowLower-Middle
3rd rowHigh
4th rowLower-Middle
5th rowUpper-Middle

Common Values

ValueCountFrequency (%)
Lower-Middle 801
26.7%
Low 741
24.7%
Upper-Middle 731
24.4%
High 727
24.2%

Length

2025-06-12T16:39:50.556861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-12T16:39:50.641765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lower-middle 801
26.7%
low 741
24.7%
upper-middle 731
24.4%
high 727
24.2%

Most occurring characters

ValueCountFrequency (%)
e 3064
13.0%
d 3064
13.0%
i 2259
9.6%
w 1542
 
6.6%
L 1542
 
6.6%
o 1542
 
6.6%
r 1532
 
6.5%
M 1532
 
6.5%
- 1532
 
6.5%
l 1532
 
6.5%
Other values (5) 4374
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3064
13.0%
d 3064
13.0%
i 2259
9.6%
w 1542
 
6.6%
L 1542
 
6.6%
o 1542
 
6.6%
r 1532
 
6.5%
M 1532
 
6.5%
- 1532
 
6.5%
l 1532
 
6.5%
Other values (5) 4374
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3064
13.0%
d 3064
13.0%
i 2259
9.6%
w 1542
 
6.6%
L 1542
 
6.6%
o 1542
 
6.6%
r 1532
 
6.5%
M 1532
 
6.5%
- 1532
 
6.5%
l 1532
 
6.5%
Other values (5) 4374
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3064
13.0%
d 3064
13.0%
i 2259
9.6%
w 1542
 
6.6%
L 1542
 
6.6%
o 1542
 
6.6%
r 1532
 
6.5%
M 1532
 
6.5%
- 1532
 
6.5%
l 1532
 
6.5%
Other values (5) 4374
18.6%

Year
Real number (ℝ)

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.049
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:50.757545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2012
Q32018
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2473057
Coefficient of variation (CV)0.0036019529
Kurtosis-1.1825567
Mean2012.049
Median Absolute Deviation (MAD)6
Skewness-0.014751223
Sum6036147
Variance52.52344
MonotonicityNot monotonic
2025-06-12T16:39:50.878534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2000 146
 
4.9%
2015 139
 
4.6%
2009 135
 
4.5%
2016 135
 
4.5%
2008 134
 
4.5%
2024 132
 
4.4%
2010 129
 
4.3%
2021 127
 
4.2%
2012 126
 
4.2%
2022 123
 
4.1%
Other values (15) 1674
55.8%
ValueCountFrequency (%)
2000 146
4.9%
2001 109
3.6%
2002 110
3.7%
2003 121
4.0%
2004 110
3.7%
2005 112
3.7%
2006 121
4.0%
2007 95
3.2%
2008 134
4.5%
2009 135
4.5%
ValueCountFrequency (%)
2024 132
4.4%
2023 123
4.1%
2022 123
4.1%
2021 127
4.2%
2020 90
3.0%
2019 117
3.9%
2018 122
4.1%
2017 117
3.9%
2016 135
4.5%
2015 139
4.6%

TB_Cases
Real number (ℝ)

Distinct2897
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24807.938
Minimum515
Maximum49985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:51.023323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum515
5-th percentile2876.6
Q112297.75
median24467
Q337031.5
95-th percentile47609.25
Maximum49985
Range49470
Interquartile range (IQR)24733.75

Descriptive statistics

Standard deviation14261.28
Coefficient of variation (CV)0.5748676
Kurtosis-1.1738689
Mean24807.938
Median Absolute Deviation (MAD)12396.5
Skewness0.05297827
Sum74423813
Variance2.033841 × 108
MonotonicityNot monotonic
2025-06-12T16:39:51.191050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17497 3
 
0.1%
13377 3
 
0.1%
746 2
 
0.1%
30513 2
 
0.1%
48164 2
 
0.1%
8114 2
 
0.1%
12686 2
 
0.1%
31040 2
 
0.1%
1447 2
 
0.1%
10747 2
 
0.1%
Other values (2887) 2978
99.3%
ValueCountFrequency (%)
515 1
< 0.1%
522 1
< 0.1%
527 1
< 0.1%
536 1
< 0.1%
545 1
< 0.1%
548 1
< 0.1%
551 1
< 0.1%
556 2
0.1%
564 1
< 0.1%
606 1
< 0.1%
ValueCountFrequency (%)
49985 1
< 0.1%
49982 1
< 0.1%
49980 1
< 0.1%
49977 1
< 0.1%
49944 1
< 0.1%
49931 1
< 0.1%
49910 1
< 0.1%
49896 1
< 0.1%
49866 1
< 0.1%
49823 1
< 0.1%

TB_Deaths
Real number (ℝ)

Distinct2591
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5090.8183
Minimum50
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:51.320399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile583
Q12660.5
median5188
Q37512.5
95-th percentile9497.05
Maximum9991
Range9941
Interquartile range (IQR)4852

Descriptive statistics

Standard deviation2846.753
Coefficient of variation (CV)0.5591936
Kurtosis-1.1815459
Mean5090.8183
Median Absolute Deviation (MAD)2439.5
Skewness-0.036694341
Sum15272455
Variance8104002.8
MonotonicityNot monotonic
2025-06-12T16:39:51.466550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4557 4
 
0.1%
8580 4
 
0.1%
3357 3
 
0.1%
8333 3
 
0.1%
3310 3
 
0.1%
1667 3
 
0.1%
2891 3
 
0.1%
9407 3
 
0.1%
3408 3
 
0.1%
6397 3
 
0.1%
Other values (2581) 2968
98.9%
ValueCountFrequency (%)
50 1
< 0.1%
54 1
< 0.1%
55 2
0.1%
58 1
< 0.1%
61 1
< 0.1%
62 1
< 0.1%
64 1
< 0.1%
66 1
< 0.1%
69 1
< 0.1%
70 1
< 0.1%
ValueCountFrequency (%)
9991 1
< 0.1%
9990 1
< 0.1%
9967 1
< 0.1%
9966 1
< 0.1%
9962 1
< 0.1%
9959 1
< 0.1%
9958 1
< 0.1%
9957 1
< 0.1%
9951 1
< 0.1%
9949 2
0.1%

TB_Incidence_Rate
Real number (ℝ)

Distinct2904
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.84548
Minimum10.06
Maximum499.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:51.608092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.06
5-th percentile36.939
Q1133.6625
median255.77
Q3374.6475
95-th percentile475.911
Maximum499.95
Range489.89
Interquartile range (IQR)240.985

Descriptive statistics

Standard deviation140.65194
Coefficient of variation (CV)0.54975346
Kurtosis-1.1928127
Mean255.84548
Median Absolute Deviation (MAD)120.93
Skewness0.023293145
Sum767536.45
Variance19782.968
MonotonicityNot monotonic
2025-06-12T16:39:51.752205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
340.14 2
 
0.1%
18.92 2
 
0.1%
339.48 2
 
0.1%
106.33 2
 
0.1%
273.13 2
 
0.1%
420.27 2
 
0.1%
422.8 2
 
0.1%
241.73 2
 
0.1%
286.92 2
 
0.1%
443.06 2
 
0.1%
Other values (2894) 2980
99.3%
ValueCountFrequency (%)
10.06 1
< 0.1%
10.12 1
< 0.1%
10.31 1
< 0.1%
10.55 1
< 0.1%
11.28 1
< 0.1%
11.77 2
0.1%
12.76 1
< 0.1%
12.88 1
< 0.1%
13.52 1
< 0.1%
13.63 1
< 0.1%
ValueCountFrequency (%)
499.95 1
< 0.1%
499.91 1
< 0.1%
499.84 1
< 0.1%
499.51 1
< 0.1%
499.14 1
< 0.1%
499.12 1
< 0.1%
498.94 1
< 0.1%
498.73 1
< 0.1%
498.71 1
< 0.1%
498.61 1
< 0.1%

TB_Mortality_Rate
Real number (ℝ)

Distinct2248
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.11921
Minimum1.01
Maximum49.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:51.893751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile3.4585
Q112.9625
median25.09
Q336.9
95-th percentile47.5505
Maximum49.99
Range48.98
Interquartile range (IQR)23.9375

Descriptive statistics

Standard deviation14.031132
Coefficient of variation (CV)0.55858173
Kurtosis-1.1837587
Mean25.11921
Median Absolute Deviation (MAD)12.025
Skewness0.043099038
Sum75357.63
Variance196.87266
MonotonicityNot monotonic
2025-06-12T16:39:52.038942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.34 6
 
0.2%
35.23 4
 
0.1%
41.94 4
 
0.1%
19.35 4
 
0.1%
11.85 4
 
0.1%
20.93 4
 
0.1%
31.2 4
 
0.1%
16.91 4
 
0.1%
32.86 4
 
0.1%
44.03 4
 
0.1%
Other values (2238) 2958
98.6%
ValueCountFrequency (%)
1.01 1
< 0.1%
1.02 1
< 0.1%
1.04 1
< 0.1%
1.06 2
0.1%
1.07 1
< 0.1%
1.09 1
< 0.1%
1.1 1
< 0.1%
1.13 1
< 0.1%
1.15 1
< 0.1%
1.17 2
0.1%
ValueCountFrequency (%)
49.99 1
< 0.1%
49.97 1
< 0.1%
49.94 1
< 0.1%
49.92 1
< 0.1%
49.88 1
< 0.1%
49.84 2
0.1%
49.82 1
< 0.1%
49.8 1
< 0.1%
49.77 1
< 0.1%
49.76 1
< 0.1%

TB_Treatment_Success_Rate
Real number (ℝ)

Distinct2178
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.484597
Minimum50
Maximum94.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:52.199101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile52.187
Q161.0675
median72.545
Q384.025
95-th percentile92.75
Maximum94.98
Range44.98
Interquartile range (IQR)22.9575

Descriptive statistics

Standard deviation13.112578
Coefficient of variation (CV)0.18090158
Kurtosis-1.2387268
Mean72.484597
Median Absolute Deviation (MAD)11.48
Skewness0.0086591801
Sum217453.79
Variance171.93971
MonotonicityNot monotonic
2025-06-12T16:39:52.342010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.77 7
 
0.2%
91.07 5
 
0.2%
64.55 5
 
0.2%
69.43 5
 
0.2%
52.53 4
 
0.1%
70.97 4
 
0.1%
57.28 4
 
0.1%
80.29 4
 
0.1%
57.34 4
 
0.1%
88.77 4
 
0.1%
Other values (2168) 2954
98.5%
ValueCountFrequency (%)
50 1
< 0.1%
50.01 2
0.1%
50.04 1
< 0.1%
50.05 1
< 0.1%
50.06 2
0.1%
50.07 1
< 0.1%
50.08 1
< 0.1%
50.09 1
< 0.1%
50.1 1
< 0.1%
50.11 1
< 0.1%
ValueCountFrequency (%)
94.98 1
 
< 0.1%
94.97 2
0.1%
94.95 1
 
< 0.1%
94.93 1
 
< 0.1%
94.91 3
0.1%
94.9 1
 
< 0.1%
94.88 1
 
< 0.1%
94.87 1
 
< 0.1%
94.82 1
 
< 0.1%
94.81 1
 
< 0.1%

Drug_Resistant_TB_Cases
Real number (ℝ)

Distinct2228
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2501.7963
Minimum13
Maximum4999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:52.481346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile241.95
Q11236
median2525.5
Q33740
95-th percentile4738.05
Maximum4999
Range4986
Interquartile range (IQR)2504

Descriptive statistics

Standard deviation1440.0324
Coefficient of variation (CV)0.57559937
Kurtosis-1.1908332
Mean2501.7963
Median Absolute Deviation (MAD)1250.5
Skewness-0.0062123884
Sum7505389
Variance2073693.3
MonotonicityNot monotonic
2025-06-12T16:39:52.634672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851 5
 
0.2%
1294 5
 
0.2%
3016 4
 
0.1%
4597 4
 
0.1%
1070 4
 
0.1%
128 4
 
0.1%
3381 4
 
0.1%
1077 4
 
0.1%
2290 4
 
0.1%
3544 4
 
0.1%
Other values (2218) 2958
98.6%
ValueCountFrequency (%)
13 1
 
< 0.1%
16 1
 
< 0.1%
17 3
0.1%
22 1
 
< 0.1%
27 2
0.1%
28 2
0.1%
29 1
 
< 0.1%
31 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
4999 2
0.1%
4994 1
 
< 0.1%
4993 2
0.1%
4988 3
0.1%
4987 1
 
< 0.1%
4984 1
 
< 0.1%
4983 2
0.1%
4982 2
0.1%
4980 1
 
< 0.1%
4976 1
 
< 0.1%

HIV_CoInfected_TB_Cases
Real number (ℝ)

Distinct2564
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4929.4993
Minimum10
Maximum9989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:52.778796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile553.85
Q12520.75
median4864
Q37328.5
95-th percentile9496.05
Maximum9989
Range9979
Interquartile range (IQR)4807.75

Descriptive statistics

Standard deviation2846.943
Coefficient of variation (CV)0.57753188
Kurtosis-1.164312
Mean4929.4993
Median Absolute Deviation (MAD)2405.5
Skewness0.045204765
Sum14788498
Variance8105084.5
MonotonicityNot monotonic
2025-06-12T16:39:52.930645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
843 3
 
0.1%
4665 3
 
0.1%
2163 3
 
0.1%
6778 3
 
0.1%
7002 3
 
0.1%
2521 3
 
0.1%
4351 3
 
0.1%
699 3
 
0.1%
8945 3
 
0.1%
3266 3
 
0.1%
Other values (2554) 2970
99.0%
ValueCountFrequency (%)
10 1
< 0.1%
16 1
< 0.1%
20 2
0.1%
25 1
< 0.1%
27 1
< 0.1%
29 1
< 0.1%
35 1
< 0.1%
36 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
ValueCountFrequency (%)
9989 1
< 0.1%
9985 1
< 0.1%
9980 1
< 0.1%
9976 1
< 0.1%
9975 2
0.1%
9973 1
< 0.1%
9969 1
< 0.1%
9959 1
< 0.1%
9958 1
< 0.1%
9956 1
< 0.1%

Population
Real number (ℝ)

Unique 

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0365352 × 108
Minimum1088106
Maximum1.3992853 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:53.070717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1088106
5-th percentile77247241
Q13.6363713 × 108
median6.9706384 × 108
Q31.0416069 × 109
95-th percentile1.3334905 × 109
Maximum1.3992853 × 109
Range1.3981972 × 109
Interquartile range (IQR)6.779698 × 108

Descriptive statistics

Standard deviation4.0181019 × 108
Coefficient of variation (CV)0.57103415
Kurtosis-1.1713213
Mean7.0365352 × 108
Median Absolute Deviation (MAD)3.4002106 × 108
Skewness0.0089935301
Sum2.1109606 × 1012
Variance1.6145143 × 1017
MonotonicityNot monotonic
2025-06-12T16:39:53.240465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
617718219 1
 
< 0.1%
11840158 1
 
< 0.1%
412757119 1
 
< 0.1%
1246189339 1
 
< 0.1%
987178400 1
 
< 0.1%
672508232 1
 
< 0.1%
1374334908 1
 
< 0.1%
1225821404 1
 
< 0.1%
953921017 1
 
< 0.1%
7173426 1
 
< 0.1%
Other values (2990) 2990
99.7%
ValueCountFrequency (%)
1088106 1
< 0.1%
1306043 1
< 0.1%
1537460 1
< 0.1%
1591145 1
< 0.1%
2034576 1
< 0.1%
2053518 1
< 0.1%
2926049 1
< 0.1%
3897933 1
< 0.1%
3955080 1
< 0.1%
4509159 1
< 0.1%
ValueCountFrequency (%)
1399285306 1
< 0.1%
1399015734 1
< 0.1%
1398738485 1
< 0.1%
1398462127 1
< 0.1%
1398359575 1
< 0.1%
1397363290 1
< 0.1%
1396240618 1
< 0.1%
1395984182 1
< 0.1%
1395817730 1
< 0.1%
1395545194 1
< 0.1%

GDP_Per_Capita
Real number (ℝ)

Distinct2920
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30046.892
Minimum501
Maximum59996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:53.386238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile3180.25
Q115212
median29707
Q344463
95-th percentile57032.15
Maximum59996
Range59495
Interquartile range (IQR)29251

Descriptive statistics

Standard deviation17172.813
Coefficient of variation (CV)0.57153376
Kurtosis-1.1932144
Mean30046.892
Median Absolute Deviation (MAD)14661
Skewness0.01184168
Sum90140677
Variance2.9490552 × 108
MonotonicityNot monotonic
2025-06-12T16:39:53.530768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14055 2
 
0.1%
24616 2
 
0.1%
9026 2
 
0.1%
30484 2
 
0.1%
17330 2
 
0.1%
47876 2
 
0.1%
18872 2
 
0.1%
22329 2
 
0.1%
55902 2
 
0.1%
14489 2
 
0.1%
Other values (2910) 2980
99.3%
ValueCountFrequency (%)
501 1
< 0.1%
503 1
< 0.1%
517 1
< 0.1%
530 1
< 0.1%
589 1
< 0.1%
590 1
< 0.1%
591 1
< 0.1%
625 1
< 0.1%
648 1
< 0.1%
659 1
< 0.1%
ValueCountFrequency (%)
59996 1
< 0.1%
59991 1
< 0.1%
59973 1
< 0.1%
59949 1
< 0.1%
59916 1
< 0.1%
59915 1
< 0.1%
59901 1
< 0.1%
59878 1
< 0.1%
59837 1
< 0.1%
59832 1
< 0.1%
Distinct2562
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5039.4857
Minimum50
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:53.670460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile564.95
Q12543.5
median5008
Q37518.75
95-th percentile9526.35
Maximum9999
Range9949
Interquartile range (IQR)4975.25

Descriptive statistics

Standard deviation2870.2696
Coefficient of variation (CV)0.56955606
Kurtosis-1.2007197
Mean5039.4857
Median Absolute Deviation (MAD)2493
Skewness0.00084137602
Sum15118457
Variance8238447.5
MonotonicityNot monotonic
2025-06-12T16:39:53.824700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7738 5
 
0.2%
3821 5
 
0.2%
1455 4
 
0.1%
9158 4
 
0.1%
4287 4
 
0.1%
1749 4
 
0.1%
7978 4
 
0.1%
2427 4
 
0.1%
1193 3
 
0.1%
8362 3
 
0.1%
Other values (2552) 2960
98.7%
ValueCountFrequency (%)
50 1
< 0.1%
52 1
< 0.1%
53 1
< 0.1%
55 2
0.1%
57 1
< 0.1%
59 1
< 0.1%
60 1
< 0.1%
69 2
0.1%
72 1
< 0.1%
86 1
< 0.1%
ValueCountFrequency (%)
9999 1
< 0.1%
9993 2
0.1%
9992 1
< 0.1%
9991 1
< 0.1%
9979 1
< 0.1%
9978 1
< 0.1%
9975 1
< 0.1%
9973 1
< 0.1%
9970 1
< 0.1%
9967 1
< 0.1%
Distinct2426
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.55871
Minimum20
Maximum89.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:53.955423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23.2195
Q137.0675
median54.775
Q371.575
95-th percentile85.9815
Maximum89.99
Range69.99
Interquartile range (IQR)34.5075

Descriptive statistics

Standard deviation20.124572
Coefficient of variation (CV)0.36886085
Kurtosis-1.2003204
Mean54.55871
Median Absolute Deviation (MAD)17.18
Skewness0.018760775
Sum163676.13
Variance404.99841
MonotonicityNot monotonic
2025-06-12T16:39:54.102632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.38 4
 
0.1%
46.43 4
 
0.1%
47.06 4
 
0.1%
38.46 4
 
0.1%
61.14 4
 
0.1%
48.57 4
 
0.1%
84.57 4
 
0.1%
72.07 4
 
0.1%
85.48 3
 
0.1%
35.54 3
 
0.1%
Other values (2416) 2962
98.7%
ValueCountFrequency (%)
20 1
< 0.1%
20.01 1
< 0.1%
20.06 2
0.1%
20.07 1
< 0.1%
20.09 1
< 0.1%
20.11 1
< 0.1%
20.12 1
< 0.1%
20.15 1
< 0.1%
20.16 1
< 0.1%
20.18 1
< 0.1%
ValueCountFrequency (%)
89.99 1
< 0.1%
89.95 1
< 0.1%
89.93 1
< 0.1%
89.89 1
< 0.1%
89.87 1
< 0.1%
89.82 1
< 0.1%
89.78 1
< 0.1%
89.76 1
< 0.1%
89.75 1
< 0.1%
89.73 1
< 0.1%

Malnutrition_Prevalence
Real number (ℝ)

Distinct2189
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.549927
Minimum5.01
Maximum49.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:54.271647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.01
5-th percentile7.209
Q116.385
median27.635
Q338.7825
95-th percentile47.761
Maximum49.97
Range44.96
Interquartile range (IQR)22.3975

Descriptive statistics

Standard deviation12.97443
Coefficient of variation (CV)0.47094245
Kurtosis-1.1917904
Mean27.549927
Median Absolute Deviation (MAD)11.19
Skewness-0.0028366176
Sum82649.78
Variance168.33583
MonotonicityNot monotonic
2025-06-12T16:39:54.409047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.23 5
 
0.2%
16.51 5
 
0.2%
44.79 4
 
0.1%
34.59 4
 
0.1%
19.55 4
 
0.1%
31.34 4
 
0.1%
9.25 4
 
0.1%
33.95 4
 
0.1%
23.85 4
 
0.1%
46.75 4
 
0.1%
Other values (2179) 2958
98.6%
ValueCountFrequency (%)
5.01 1
 
< 0.1%
5.03 1
 
< 0.1%
5.06 3
0.1%
5.08 1
 
< 0.1%
5.1 1
 
< 0.1%
5.11 1
 
< 0.1%
5.13 1
 
< 0.1%
5.15 2
0.1%
5.16 2
0.1%
5.19 1
 
< 0.1%
ValueCountFrequency (%)
49.97 1
< 0.1%
49.96 2
0.1%
49.95 1
< 0.1%
49.94 1
< 0.1%
49.92 1
< 0.1%
49.88 1
< 0.1%
49.87 2
0.1%
49.85 1
< 0.1%
49.82 1
< 0.1%
49.8 2
0.1%

Smoking_Prevalence
Real number (ℝ)

Distinct2029
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.439453
Minimum5.02
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:54.543221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.02
5-th percentile6.689
Q113.75
median22.195
Q331.5225
95-th percentile38.2405
Maximum40
Range34.98
Interquartile range (IQR)17.7725

Descriptive statistics

Standard deviation10.179438
Coefficient of variation (CV)0.45364018
Kurtosis-1.2085205
Mean22.439453
Median Absolute Deviation (MAD)8.87
Skewness0.021914773
Sum67318.36
Variance103.62095
MonotonicityNot monotonic
2025-06-12T16:39:54.683480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.87 5
 
0.2%
37.35 5
 
0.2%
10.04 5
 
0.2%
6 5
 
0.2%
17.52 5
 
0.2%
15.89 5
 
0.2%
25.28 4
 
0.1%
15.36 4
 
0.1%
20.4 4
 
0.1%
11.32 4
 
0.1%
Other values (2019) 2954
98.5%
ValueCountFrequency (%)
5.02 2
0.1%
5.05 1
 
< 0.1%
5.06 3
0.1%
5.08 1
 
< 0.1%
5.09 1
 
< 0.1%
5.11 1
 
< 0.1%
5.12 4
0.1%
5.13 2
0.1%
5.14 2
0.1%
5.15 1
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
39.96 2
0.1%
39.95 1
 
< 0.1%
39.93 1
 
< 0.1%
39.92 1
 
< 0.1%
39.91 1
 
< 0.1%
39.88 2
0.1%
39.87 1
 
< 0.1%
39.86 3
0.1%
39.85 3
0.1%

TB_Doctors_Per_100K
Real number (ℝ)

Distinct949
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.01523
Minimum0.1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:54.841772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.64
Q12.52
median4.93
Q37.48
95-th percentile9.52
Maximum10
Range9.9
Interquartile range (IQR)4.96

Descriptive statistics

Standard deviation2.8511242
Coefficient of variation (CV)0.56849321
Kurtosis-1.195264
Mean5.01523
Median Absolute Deviation (MAD)2.47
Skewness0.036692133
Sum15045.69
Variance8.1289093
MonotonicityNot monotonic
2025-06-12T16:39:54.972566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.17 10
 
0.3%
6.72 9
 
0.3%
4.03 9
 
0.3%
4.83 8
 
0.3%
8.68 8
 
0.3%
6.6 8
 
0.3%
6.71 8
 
0.3%
1.18 8
 
0.3%
1.12 8
 
0.3%
0.94 8
 
0.3%
Other values (939) 2916
97.2%
ValueCountFrequency (%)
0.1 2
 
0.1%
0.11 3
0.1%
0.12 5
0.2%
0.14 2
 
0.1%
0.15 3
0.1%
0.16 2
 
0.1%
0.17 2
 
0.1%
0.18 1
 
< 0.1%
0.19 2
 
0.1%
0.2 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9.99 1
 
< 0.1%
9.98 8
0.3%
9.97 2
 
0.1%
9.96 7
0.2%
9.95 3
 
0.1%
9.94 4
0.1%
9.93 4
0.1%
9.92 2
 
0.1%
9.91 2
 
0.1%

TB_Hospitals_Per_Million
Real number (ℝ)

Distinct1536
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.268077
Minimum0.51
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:55.107203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile1.4695
Q15.13
median10.39
Q315.36
95-th percentile18.991
Maximum20
Range19.49
Interquartile range (IQR)10.23

Descriptive statistics

Standard deviation5.7174378
Coefficient of variation (CV)0.55681682
Kurtosis-1.2622465
Mean10.268077
Median Absolute Deviation (MAD)5.095
Skewness-0.0096869577
Sum30804.23
Variance32.689095
MonotonicityNot monotonic
2025-06-12T16:39:55.274402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.68 8
 
0.3%
11.2 7
 
0.2%
2.08 7
 
0.2%
18.53 6
 
0.2%
16.35 6
 
0.2%
12.58 5
 
0.2%
15.59 5
 
0.2%
16.43 5
 
0.2%
4.95 5
 
0.2%
16.89 5
 
0.2%
Other values (1526) 2941
98.0%
ValueCountFrequency (%)
0.51 2
0.1%
0.53 1
 
< 0.1%
0.54 2
0.1%
0.55 3
0.1%
0.57 1
 
< 0.1%
0.58 1
 
< 0.1%
0.59 3
0.1%
0.6 1
 
< 0.1%
0.61 1
 
< 0.1%
0.63 2
0.1%
ValueCountFrequency (%)
20 2
0.1%
19.99 1
 
< 0.1%
19.98 2
0.1%
19.97 2
0.1%
19.96 1
 
< 0.1%
19.95 1
 
< 0.1%
19.93 3
0.1%
19.92 3
0.1%
19.89 2
0.1%
19.88 3
0.1%

Access_To_Health_Services
Real number (ℝ)

Distinct2405
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.597103
Minimum30.01
Maximum99.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:55.416656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30.01
5-th percentile33.2295
Q147.24
median64.63
Q381.3425
95-th percentile96.021
Maximum99.96
Range69.95
Interquartile range (IQR)34.1025

Descriptive statistics

Standard deviation19.990994
Coefficient of variation (CV)0.30947199
Kurtosis-1.1767319
Mean64.597103
Median Absolute Deviation (MAD)17.1
Skewness0.0095056317
Sum193791.31
Variance399.63984
MonotonicityNot monotonic
2025-06-12T16:39:55.551973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.47 4
 
0.1%
68.84 4
 
0.1%
75.41 4
 
0.1%
82.69 4
 
0.1%
46.76 4
 
0.1%
81.59 4
 
0.1%
49.23 4
 
0.1%
86.44 4
 
0.1%
73.97 4
 
0.1%
51.95 4
 
0.1%
Other values (2395) 2960
98.7%
ValueCountFrequency (%)
30.01 1
< 0.1%
30.02 1
< 0.1%
30.03 1
< 0.1%
30.04 2
0.1%
30.05 2
0.1%
30.06 1
< 0.1%
30.07 2
0.1%
30.1 1
< 0.1%
30.11 1
< 0.1%
30.17 1
< 0.1%
ValueCountFrequency (%)
99.96 1
< 0.1%
99.9 1
< 0.1%
99.86 1
< 0.1%
99.84 1
< 0.1%
99.79 1
< 0.1%
99.76 1
< 0.1%
99.73 1
< 0.1%
99.65 1
< 0.1%
99.63 1
< 0.1%
99.6 1
< 0.1%

BCG_Vaccination_Coverage
Real number (ℝ)

Distinct2263
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.588197
Minimum50.02
Maximum98.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:55.692057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.02
5-th percentile52.3185
Q162.5175
median74.87
Q386.645
95-th percentile96.48
Maximum98.99
Range48.97
Interquartile range (IQR)24.1275

Descriptive statistics

Standard deviation14.124815
Coefficient of variation (CV)0.18937064
Kurtosis-1.1865203
Mean74.588197
Median Absolute Deviation (MAD)12.06
Skewness-0.025002436
Sum223764.59
Variance199.51039
MonotonicityNot monotonic
2025-06-12T16:39:55.843171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.02 5
 
0.2%
64.08 5
 
0.2%
84.08 4
 
0.1%
68.36 4
 
0.1%
98.16 4
 
0.1%
53.13 4
 
0.1%
50.48 4
 
0.1%
96.78 4
 
0.1%
66.01 4
 
0.1%
87.83 4
 
0.1%
Other values (2253) 2958
98.6%
ValueCountFrequency (%)
50.02 1
< 0.1%
50.03 2
0.1%
50.04 1
< 0.1%
50.06 1
< 0.1%
50.07 1
< 0.1%
50.08 1
< 0.1%
50.12 1
< 0.1%
50.14 1
< 0.1%
50.15 1
< 0.1%
50.16 1
< 0.1%
ValueCountFrequency (%)
98.99 3
0.1%
98.98 2
0.1%
98.96 1
 
< 0.1%
98.94 1
 
< 0.1%
98.91 1
 
< 0.1%
98.9 1
 
< 0.1%
98.8 1
 
< 0.1%
98.79 1
 
< 0.1%
98.72 3
0.1%
98.71 1
 
< 0.1%

HIV_Testing_Coverage
Real number (ℝ)

Distinct2455
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.49869
Minimum20
Maximum89.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2025-06-12T16:39:55.976935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23.428
Q138.24
median55.825
Q373.4975
95-th percentile86.8005
Maximum89.98
Range69.98
Interquartile range (IQR)35.2575

Descriptive statistics

Standard deviation20.250306
Coefficient of variation (CV)0.364879
Kurtosis-1.2034151
Mean55.49869
Median Absolute Deviation (MAD)17.66
Skewness-0.042663346
Sum166496.07
Variance410.07491
MonotonicityNot monotonic
2025-06-12T16:39:56.116323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.09 4
 
0.1%
73.88 4
 
0.1%
21.72 4
 
0.1%
55.65 4
 
0.1%
86.22 4
 
0.1%
55.26 3
 
0.1%
26.85 3
 
0.1%
80.25 3
 
0.1%
73.49 3
 
0.1%
56.08 3
 
0.1%
Other values (2445) 2965
98.8%
ValueCountFrequency (%)
20 1
< 0.1%
20.07 1
< 0.1%
20.09 2
0.1%
20.1 1
< 0.1%
20.13 1
< 0.1%
20.15 1
< 0.1%
20.17 2
0.1%
20.18 1
< 0.1%
20.19 2
0.1%
20.21 1
< 0.1%
ValueCountFrequency (%)
89.98 1
< 0.1%
89.96 1
< 0.1%
89.94 1
< 0.1%
89.92 1
< 0.1%
89.88 1
< 0.1%
89.8 1
< 0.1%
89.78 2
0.1%
89.75 2
0.1%
89.71 1
< 0.1%
89.7 1
< 0.1%

Interactions

2025-06-12T16:39:45.776065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.302783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:03.469393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:05.610775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:07.746557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:10.594533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:14.578490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:16.634445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:18.778896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:21.461041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.393571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:26.592390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.148826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.339731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:33.415756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.023012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.122248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.407542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:43.634910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:45.901532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.411785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:03.579399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:05.721063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:07.849104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:10.762907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:14.675881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:16.740895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:18.883776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:21.624686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.506294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:27.152022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.264112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.438873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:33.527793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.138518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.245511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.523795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:43.737950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:46.014317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.527164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:03.688510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:05.831398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:07.957030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:10.921464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:14.775910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:16.847743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:18.985553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:21.780674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.607816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:27.262630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.379674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.549746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:33.696025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.239993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.359486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.638508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:43.863557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:46.962376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.637161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:03.791569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:05.938028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:08.070138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:11.030633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:14.878135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:16.951967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:19.452832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:21.952084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.725180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:27.365446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.491819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.650263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:33.861165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.343537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.473281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.769058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:43.965184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:47.129816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.749956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:03.891248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:06.046360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:08.188562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:11.137337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:14.980007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:17.061936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:19.553122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:22.130751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.832964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:27.471898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.608903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.754350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:34.042217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.454684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.590773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.873689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:44.077438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:47.300101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:01.866354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:04.006732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:06.172289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:08.348331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:11.238329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:15.080562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:17.175181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:19.666483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:22.287557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.939827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:27.576506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.715307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.856058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:34.206861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:37.576268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.715296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.990520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-12T16:39:14.444836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-12T16:39:18.659033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:21.296011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:24.277709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:26.480692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:29.026623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:31.226538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:33.287826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:36.902981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:39.013180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:41.287899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:43.516625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-12T16:39:45.666389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-12T16:39:56.249547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Access_To_Health_ServicesBCG_Vaccination_CoverageCountryDrug_Resistant_TB_CasesGDP_Per_CapitaHIV_CoInfected_TB_CasesHIV_Testing_CoverageHealth_Expenditure_Per_CapitaIncome_LevelMalnutrition_PrevalencePopulationRegionSmoking_PrevalenceTB_CasesTB_DeathsTB_Doctors_Per_100KTB_Hospitals_Per_MillionTB_Incidence_RateTB_Mortality_RateTB_Treatment_Success_RateUrban_Population_PercentageYear
Access_To_Health_Services1.0000.0080.0230.0390.001-0.0120.0310.0050.023-0.0080.0040.000-0.0200.009-0.0440.034-0.0060.001-0.018-0.0240.013-0.007
BCG_Vaccination_Coverage0.0081.0000.000-0.0190.0100.0180.0150.0310.0190.013-0.0420.000-0.0130.008-0.0010.0220.010-0.007-0.0110.0160.0230.018
Country0.0230.0001.0000.0150.0060.0280.0000.0130.0000.0150.0000.0270.0190.0180.0130.0140.0060.0000.0170.0100.0190.023
Drug_Resistant_TB_Cases0.039-0.0190.0151.0000.031-0.0140.0090.0010.0090.010-0.0090.000-0.0050.006-0.0110.012-0.003-0.005-0.0010.0050.020-0.004
GDP_Per_Capita0.0010.0100.0060.0311.000-0.0340.0230.0180.0000.0390.0280.000-0.0020.0110.008-0.0030.0100.0030.010-0.026-0.012-0.014
HIV_CoInfected_TB_Cases-0.0120.0180.028-0.014-0.0341.000-0.005-0.0040.000-0.006-0.0060.0000.0260.0170.0130.024-0.0110.0110.000-0.010-0.007-0.016
HIV_Testing_Coverage0.0310.0150.0000.0090.023-0.0051.0000.0160.016-0.027-0.0060.0000.0080.0030.024-0.016-0.017-0.0020.0080.0450.0120.026
Health_Expenditure_Per_Capita0.0050.0310.0130.0010.018-0.0040.0161.0000.0000.0240.0120.0000.0410.006-0.0350.008-0.0090.0080.0010.015-0.0270.000
Income_Level0.0230.0190.0000.0090.0000.0000.0160.0001.0000.0000.0310.0000.0000.0000.0340.0360.0000.0000.0000.0000.0130.024
Malnutrition_Prevalence-0.0080.0130.0150.0100.039-0.006-0.0270.0240.0001.0000.0160.0210.0240.010-0.0100.0020.009-0.021-0.012-0.0040.017-0.014
Population0.004-0.0420.000-0.0090.028-0.006-0.0060.0120.0310.0161.0000.0330.012-0.041-0.0210.012-0.0050.0180.0170.0040.011-0.009
Region0.0000.0000.0270.0000.0000.0000.0000.0000.0000.0210.0331.0000.0170.0230.0070.0320.0000.0000.0090.0240.0140.012
Smoking_Prevalence-0.020-0.0130.019-0.005-0.0020.0260.0080.0410.0000.0240.0120.0171.0000.010-0.002-0.006-0.006-0.0320.0260.023-0.016-0.007
TB_Cases0.0090.0080.0180.0060.0110.0170.0030.0060.0000.010-0.0410.0230.0101.000-0.015-0.015-0.0180.0080.005-0.005-0.006-0.004
TB_Deaths-0.044-0.0010.013-0.0110.0080.0130.024-0.0350.034-0.010-0.0210.007-0.002-0.0151.0000.001-0.0140.0100.0040.0190.0090.020
TB_Doctors_Per_100K0.0340.0220.0140.012-0.0030.024-0.0160.0080.0360.0020.0120.032-0.006-0.0150.0011.000-0.010-0.008-0.032-0.025-0.0060.020
TB_Hospitals_Per_Million-0.0060.0100.006-0.0030.010-0.011-0.017-0.0090.0000.009-0.0050.000-0.006-0.018-0.014-0.0101.0000.0190.0190.0030.002-0.006
TB_Incidence_Rate0.001-0.0070.000-0.0050.0030.011-0.0020.0080.000-0.0210.0180.000-0.0320.0080.010-0.0080.0191.000-0.032-0.004-0.013-0.045
TB_Mortality_Rate-0.018-0.0110.017-0.0010.0100.0000.0080.0010.000-0.0120.0170.0090.0260.0050.004-0.0320.019-0.0321.000-0.000-0.0180.022
TB_Treatment_Success_Rate-0.0240.0160.0100.005-0.026-0.0100.0450.0150.000-0.0040.0040.0240.023-0.0050.019-0.0250.003-0.004-0.0001.000-0.0190.034
Urban_Population_Percentage0.0130.0230.0190.020-0.012-0.0070.012-0.0270.0130.0170.0110.014-0.016-0.0060.009-0.0060.002-0.013-0.018-0.0191.0000.016
Year-0.0070.0180.023-0.004-0.014-0.0160.0260.0000.024-0.014-0.0090.012-0.007-0.0040.0200.020-0.006-0.0450.0220.0340.0161.000

Missing values

2025-06-12T16:39:49.516546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-12T16:39:49.735151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryRegionIncome_LevelYearTB_CasesTB_DeathsTB_Incidence_RateTB_Mortality_RateTB_Treatment_Success_RateDrug_Resistant_TB_CasesHIV_CoInfected_TB_CasesPopulationGDP_Per_CapitaHealth_Expenditure_Per_CapitaUrban_Population_PercentageMalnutrition_PrevalenceSmoking_PrevalenceTB_Doctors_Per_100KTB_Hospitals_Per_MillionAccess_To_Health_ServicesBCG_Vaccination_CoverageHIV_Testing_Coverage
0IndonesiaAsiaUpper-Middle2002141283846150.5818.9954.52397775761184015825516318050.4928.8422.904.1015.4652.3895.8788.39
1BrazilNorth AmericaLower-Middle20171069342167.8044.2088.194179328441275711926300552924.0628.6533.798.183.0646.9563.7835.60
2NigeriaSouth AmericaHigh200624736316285.776.5264.5549613283124618933930771170381.7738.696.632.264.9540.8868.9177.81
3RussiaAfricaLower-Middle2014360906420146.4624.8669.774012625398717840035730835442.1920.3815.008.3313.6862.2071.1484.91
4IndonesiaEuropeUpper-Middle202033538491056.8710.5264.572342347967250823238391802464.9739.2818.006.604.3553.8855.1149.89
5BangladeshNorth AmericaLower-Middle202149588377990.7843.1963.9515835942137433490830121954162.5226.8339.142.2813.7482.1469.8087.34
6USAAfricaLow20198141801295.2313.4283.633278657412258214044678235460.9133.715.120.7519.5546.5887.9734.15
7IndonesiaSouth AmericaLower-Middle201794305560422.9232.0483.6443855372106111463832948697056.8048.4215.787.7716.7181.5491.5086.70
8NigeriaAsiaLower-Middle202471808669149.329.1173.304892464685703304113776994332.7026.2137.562.273.6250.1793.4283.20
9RussiaSouth AmericaUpper-Middle200217576811360.2020.8479.596955356100422184115328386439.6423.8315.738.9010.6571.4950.7124.63
CountryRegionIncome_LevelYearTB_CasesTB_DeathsTB_Incidence_RateTB_Mortality_RateTB_Treatment_Success_RateDrug_Resistant_TB_CasesHIV_CoInfected_TB_CasesPopulationGDP_Per_CapitaHealth_Expenditure_Per_CapitaUrban_Population_PercentageMalnutrition_PrevalenceSmoking_PrevalenceTB_Doctors_Per_100KTB_Hospitals_Per_MillionAccess_To_Health_ServicesBCG_Vaccination_CoverageHIV_Testing_Coverage
2990PakistanAfricaLow2011187928139270.3916.0173.48783700282531281944637264121.0721.3911.198.177.5233.1187.8143.89
2991USASouth AmericaLow2016189338506144.0634.9856.8733874857375125630586889188.4519.179.579.514.1781.5597.4521.58
2992IndiaAfricaLower-Middle2015150683781464.133.1984.1480399808391112042456340445.6441.0311.374.105.6788.0985.5582.70
2993IndonesiaNorth AmericaHigh2019459246377250.2124.7479.949714600137181253343782229280.8547.5437.690.5914.1434.7782.6250.03
2994BangladeshSouth AmericaLow201599663367422.5824.8163.374702609763951332530484494933.0838.6610.762.4719.2052.6194.2662.08
2995RussiaAfricaLower-Middle2018260212096242.908.9455.261420372873300779918921628651.2612.8230.331.114.5385.4463.1933.62
2996RussiaAsiaLower-Middle2014371723194361.9642.0387.4011478211139431964142422253023.0922.6111.463.519.5864.5263.5829.94
2997IndiaEuropeLow2011159673271175.0740.0480.3446765091623950718174360722.8139.1739.146.4919.8378.6481.4226.08
2998PakistanEuropeHigh2020325195114316.168.4850.311578993995299957556769353686.0345.8117.835.2418.6765.1693.2275.91
2999IndonesiaAsiaUpper-Middle2009109199495254.2018.8378.25448225886177182195837621176.7449.9419.422.507.3798.4880.2565.25